TITLE:
Gapped Motif Discovery with Multi-Objective Genetic Algorithm
AUTHORS:
U. Angela Makolo, Salihu O. Suberu
KEYWORDS:
Genetic Algorithm, Motif Discovery, Multi-Objective Optimization
JOURNAL NAME:
Open Access Library Journal,
Vol.3 No.3,
March
30,
2016
ABSTRACT:
Motif discovery is one of the fundamental problems that have important applications
in identifying drug targets and regulatory sites. Regulatory sites on DNA
sequence normally correspond to shared conservative sequence patterns among the
regulatory regions of correlated genes. These conserved sequence patterns are
called motifs. Identifying motifs and corresponding instances is very
important, so biologists can investigate the interactions between DNA and
proteins, gene regulation, cell development and cell reaction under
physiological and pathological conditions. In this work, we developed a motif
finding algorithm based on a multi-objective genetic algorithm technique and
incorporated the hypergeometric scoring function to enable it discover gapped motifs
from organisms with challenging genomic structure such as the malaria parasite.
The runtime performance of our resulting algorithm, EMOGAMOD (Extended Multi
Objective Genetic Algorithm MOtif Discovery) was evaluated with that of some
common motif discovery algorithms and the result was remarkable.